Diabetes mellitus is a metabolic disorder and is a global challenge to the current medicinal chemists and pharmacologists. This research has been designed to isolate and evaluate antidiabetic bioactives from Fragaria indica. The crude extracts, semi-purified and pure bioactives have been used in all in vitro assays. The in vitro α-glucosidase, α-amylase and DPPH free radical activities have been performed on all plant samples. The initial activities showed that ethyl acetate (Fi.EtAc) was the potent fraction in all the assays. This fraction was initially semi-purified to obtain Fi.EtAc 1–3. Among the semi-purified fractions, Fi.EtAc 2 was dominant, exhibiting potent IC50 values in all the in vitro assays. Based on the potency and availability of materials, Fi.EtAc 2 was subjected to further purification to obtain compounds 1 (2,4-dichloro-6-hydroxy-3,5-dimethoxytoluene) and 2 (2-methyl-6-(4-methylphenyl)-2-hepten-4-one). The two isolated compounds were characterized by mass and NMR analyses. The compounds 1 and 2 showed excellent inhibitions against α-glucosidase (21.45 for 1 and 15.03 for 2 μg/mL), α-amylase (17.65 and 16.56 μg/mL) and DPPH free radicals (7.62 and 14.30 μg/mL). Our study provides baseline research for the antidiabetic bioactives exploration from Fragaria indica. The bioactive compounds can be evaluated in animals-based antidiabetic activity in future.

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19’s impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students’ health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student’s remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.

Geological CO2 sequestration (GCS) has been proposed as an effective approach to mitigate carbon emissions in the atmosphere. Uncertainty and sensitivity analysis of the fate of CO2 dynamics and storage are essential aspects of large-scale reservoir simulations. This work presents a rigorous machine learning-assisted (ML) workflow for the uncertainty and global sensitivity analysis of CO2 storage prediction in deep saline aquifers. The proposed workflow comprises three main steps: The first step concerns dataset generation, in which we identify the uncertainty parameters impacting CO2 flow and transport and then determine their corresponding ranges and distributions. The training data samples are generated by combining the Latin Hypercube Sampling (LHS) technique with high-resolution simulations. The second step involves ML model development based on a data-driven ML model, which is generated to map the nonlinear relationship between the input parameters and corresponding output interests from the previous step. We show that using Bayesian optimization significantly accelerates the tuning process of hyper-parameters, which is vastly superior to a traditional trial–error analysis. In the third step, uncertainty and global sensitivity analysis are performed using Monte Carlo simulations applied to the optimized surrogate. This step is performed to explore the time-dependent uncertainty propagation of model outputs. The key uncertainty parameters are then identified by calculating the Sobol indices based on the global sensitivity analysis. The proposed workflow is accurate and efficient and could be readily implemented in field-scale CO2 sequestration in deep saline aquifers.

High throughput screening of synthetic compounds against vital enzymes is the way forward for the determination of potent enzyme inhibitors. In-vitro high throughput library screening of 258 synthetic compounds (comp. 1–258), was performed against α-glucosidase. The active compounds out of this library were investigated for their mode of inhibition and binding affinities towards α-glucosidase through kinetics as well as molecular docking studies. Out of all the compounds selected for this study, 63 compounds were found active within the IC50 range of 3.2 μM to 50.0 μM. The most potent inhibitor of α-glucosidase out of this library was the derivative of an oxadiazole (comp. 25). It showed the IC50 value of 3.23 ± 0.8 μM. Other highly active compounds were the derivatives of ethyl-thio benzimidazolyl acetohydrazide with IC50 values of 6.1 ± 0.5 μM (comp. 228), 6.84 ± 1.3 μM (comp. 212), 7.34 ± 0.3 μM (comp. 230) and 8.93 ± 1.0 μM (comp. 210). For comparison, the standard (acarbose) showed IC50 = 378.2 ± 0.12 μM. Kinetic studies of oxadiazole (comp. 25) and ethylthio benzimidazolyl acetohydrazide (comp. 228) derivatives indicated that Vmax and Km, both change with changing concentrations of inhibitors which suggests an un-competitive mode of inhibition. Molecular docking studies of these derivatives with the active site of α-glucosidase (PDB ID:1XSK), revealed that these compounds mostly interact with acidic or basic amino acid residues through conventional hydrogen bonds along with other hydrophobic interactions. The binding energy values of compounds 25, 228, and 212 were -5.6, -8.7 and -5.4 kcal.mol-1 whereas RMSD values were 0.6, 2.0, and 1.7 Å, respectively. For comparison, the co-crystallized ligand showed a binding energy value of -6.6 kcal.mol-1 along with an RMSD value of 1.1 Å. Our study predicted several series of compounds as active inhibitors of α-glucosidase including some highly potent inhibitors.

Globally, pathogenic microbes have reached a worrisome level of antibiotic resistance. Our work aims to identify and isolate the active components from the bioactive Ficus retusa bark extract and assess the potential synergistic activity of the most major compounds’ constituents with the antibiotic tetracycline against certain pathogenic bacterial strains. The phytochemical screening of an acetone extract of F. retusa bark using column chromatography led to the identification of 10 phenolic components. The synergistic interaction of catechin and chlorogenic acid as the most major compounds with tetracycline was evaluated by checkerboard assay followed by time-kill assay, against Bacillus cereus, Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumonia, and Salmonella typhi with fraction inhibitory concentration index values (FICI) of 0.38, 0.43, 0.38, 0.38, 0.38, and 0.75 for catechin and 0.38, 0.65, 0.38, 0.63, 0.38, and 0.75 for chlorogenic acid. The combination of catechin and chlorogenic acid with tetracycline significantly enhanced antibacterial action against gram-positive and gram-negative microorganisms; therefore, catechin and chlorogenic acid combinations with tetracycline could be employed as innovative and safe antibiotics to combat microbial resistance.